Automatic Workout Data Trimming and Reclassification
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Solution Overview
Problem
Fitness tracking devices require users to manually start, pause, and stop workouts, leading to inaccuracies due to warm-ups, cool-downs, and breaks being included in workout data, which can disrupt the user's experience and reduce the relevance of performance metrics.
Innovation Solution
A method and apparatus for automatically trimming and reclassifying workout data by identifying non-workout intervals and prompting users to remove or reclassify them, allowing for the generation of trimmed and reclassified data depictions for improved accuracy and user experience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If users manually start and stop workout tracking, then the tracking process is simple and direct, but the workout data includes non-workout intervals (warm-up, cool-down, breaks) which reduces measurement precision
Solution Approach 1:
The system automatically detects and trims non-workout intervals from the recorded data without requiring user intervention during the workout. The processor analyzes the recorded data to identify warm-up, cool-down, and break periods, then automatically removes these segments to provide accurate workout metrics.
2Productivity
If users start tracking before warm-up and stop after cool-down, then the tracking process is uninterrupted and simple, but the performance metrics become less relevant due to inclusion of non-workout data
Solution Approach 1:
The system extracts and separates non-workout intervals from the continuous recorded data. The processor identifies segments corresponding to warm-up, cool-down, and breaks, then extracts these portions to create a trimmed dataset that contains only the actual workout period, preserving both tracking efficiency and metric relevance.
3Measurement precision
If the system automatically processes workout data to remove non-workout intervals, then the measurement precision improves, but the device complexity increases
Solution Approach 1:
The processor automatically analyzes the recorded data to identify and trim non-workout intervals without requiring additional user input or complex external systems. The device uses its own computational resources to perform the analysis and data refinement, maintaining simplicity while improving accuracy.
Data Source
AI summary
A system and method for automatically trimming and reclassifying workout data is disclosed. The system receives data associated with a workout of a user from at least one sensor associated with the user, the workout being classified as a first type of workout. The system processes the data to identify at least one time interval during the workout that does not correspond to the first workout type. The system prompts the user to select whether to remove or reclassify a subset of the data that is associated with the identified time interval. If the user chooses to do so, the system removes or reclassifies the subset of the data that is associated with the identified time interval. The system generates and provides workout depictions using the data, at least one of which illustrates only the remaining data that was not removed or reclassified.